Databricks Lead

Blumetra; Hyderabad; Hybrid; INR 900,000 - 1,400,000; Full time

Job summary

Blumetra in Hyderabad (Hybrid) seeks a hands-on Databricks Lead to design, build and productionize governed financial data products on an Azure Databricks lakehouse. You will own end-to-end Databricks solutions, from ingestion to row-level security, while steering a small engineering team and collaborating with finance, security, and platform teams.

Responsibilities include architecture, ingestion, data modelling, governance and performance tuning to deliver reliable, scalable data platforms for

Qualifications

• Must have delivered a production Databricks pipeline with Unity Catalog row-level security in a regulated or finance setting.
• File-based, event-triggered ingestion framework with manifest or control-file validation.
• Financial planning or EPM data integration into a lakehouse or warehouse.

Responsibilities

• Architect and provide technical leadership; drive solution design with stakeholders.
• Lead 2–4 data engineers; set coding, testing and documentation standards.
• Oversee landing zones, medallion layers, and Unity Catalog governance.

Skills

Databricks core

Unity Catalog

Fine-grained security

Delta Lake

Performance

PySpark

SQL

Python engineering

Azure

DevOps

Data modelling

Data quality

Tools

Databricks

Azure

Job description

Job Title: Databricks – Lead Data Engineer

Work Location: Hyderabad (Hybrid)

Experience: 7 – 10 years

Role summary

We are looking for a hands-on Databricks Lead to design, build and take to production governed financial data products on an Azure Databricks lakehouse. You will own the Databricks solution end to end – file-based ingestion, medallion pipelines, hierarchy modelling, row-level security, zero-outage publishing and performance – and lead a small engineering team while working closely with source-system owners, finance stakeholders, and security and platform teams.

What you will build

A typical engagement publishes multi-dimensional financial data (for example a management P&L with entity, department, project, product, version, period and account dimensions) from an enterprise planning system as a single, row-secured consumption table in Databricks, refreshed several times a day. The quality bar:

• Exact reconciliation to source control totals; no data loss.
• Full fidelity of hierarchies, alternate hierarchies, aliases and attributes.
• Zero consumer outage during refresh; a failed run never replaces the last good data.
• Row-level security that matches the source system's access rules.
• Sub-second response for agreed benchmark queries.
• Additive schema changes absorbed without code changes; full rebuild from Git.

Key responsibilities

Architecture and technical leadership

• Own the Databricks solution design and drive open decisions with stakeholders: landing zone, publish mechanism, orchestration pattern, compute, retention and recovery targets.
• Translate integration specifications into buildable designs, data contracts and a delivery plan; lead design reviews with integration architects and platform teams.
• Lead, mentor and review the work of 2–4 data engineers; set coding, testing and documentation standards.
• Raise risks early (data volume, security parity, BI connectivity) and propose options with trade-offs.

Platform setup and governance (Unity Catalog)

• Set up catalogs, schemas, storage credentials, external locations and volumes for landing zones and medallion layers across dev, test and prod.
• Define the grant model with account-level groups synced from Microsoft Entra ID; configure service principals for jobs and CI/CD.
• Work with Azure network teams on private endpoints for ADLS and Network Connectivity Configurations for serverless compute.

Ingestion and orchestration

• Build manifest-gated, file-arrival-triggered Databricks Workflows with task dependencies, task values, condition-task publish gates, queueing, retries, timeouts and notifications.
• Implement Bronze ingestion from delimited files with checksum and row-count verification against a JSON manifest.
• Guarantee idempotent reruns and safe handling of overlapping or late runs.

Data modelling and transformation (Silver / Gold)

• Build PySpark logic to validate and flatten ragged parent-child hierarchies, detecting cycles, orphans, multi-parent members and non-unit weights.
• Handle alternate hierarchies through configuration, as flattened tables or bridge tables.
• Pivot long-form attributes and aliases into columns; build a contract-driven Gold generator that absorbs additive schema changes automatically.
• Design wide, denormalized consumption tables, including leaf/consolidated grain flags to prevent double counting.

Data quality and reconciliation

• Implement blocking quality checks (contract, referential integrity, hierarchy, typing, enrichment, security) and exact reconciliation to source control totals.
• Build control tables (run log, check results, reconciliation, schema contract) and an operations dashboard.

Security

• Implement source-system security parity: map source users to Entra identities, resolve group and member-level rights into a security lookup table, and enforce them with a Unity Catalog row-filter function.
• Keep data and security consistent during publish; define break-glass access, ownership and audit monitoring.
• Advise BI teams on per-user connectivity (SSO / DirectQuery) so row-level security holds end to end.

Publishing and performance

• Implement zero-outage publishing with atomic Delta overwrites or blue/green swaps, plus rollback via time travel.
• Tune for sub-second queries: liquid clustering, data-skipping statistics, predictive optimization, Photon, SQL Warehouse sizing and row-filter cost; run benchmark tests with real access profiles.

DevOps, testing and operations

• Package everything as Databricks Asset Bundles with CI/CD (GitHub Actions or Azure DevOps) across dev, test and prod.
• Own the test strategy: unit tests, contract and fixture tests, integration, SIT reconciliation, security UAT and performance tests.
• Write the runbook (rerun, rollback, full rebuild, schema change) and hand over to production support.

Collaboration

• Agree file contracts, manifests and control totals with source-system administrators.
• Work with finance users to define benchmark queries and validate numbers during UAT and parallel runs.
• Report progress, risks and decisions to stakeholders and delivery leadership.

Required skills

Area

Databricks core

Unity Catalog

Fine-grained security

Delta Lake

Performance

PySpark and SQL

Python engineering

Azure

DevOps

Data modelling

Data quality

Must have delivered at least one of:

• A production Databricks pipeline with Unity Catalog row-level security in a regulated or finance setting.
• A file-based, event-triggered ingestion framework with manifest or control-file validation.
• A financial planning or EPM data integration into a lakehouse or warehouse.

Preferred skills, qualifications and attributes

Nice to have

• Enterprise planning / EPM platforms: multi-dimensional cubes, dimensions and hierarchies, rules and consolidations, member-level security, and exporting data from them.
• FP&A domain knowledge: management P&A, budget and forecast versions, adjustments and allocations, spend by product or project, GAAP vs non-GAAP.
• Power BI with Databricks: DirectQuery, Entra SSO pass-through, impact of import models on row-level security.
• Lakeflow Declarative Pipelines (DLT) and expectations, Auto Loader, file events on external locations.
• Databricks system tables for cost and job monitoring; Lakeview dashboards and SQL alerts.
• Experience in regulated environments (SOX controls, audit trails, change management).

Certifications (any is a plus)

• Databricks Certified Data Engineer Professional
• Databricks Certified Data Engineer Associate
• Microsoft Certified: Azure Data Engineer Associate or Azure Solutions Architect Expert